Temporal and spatial patterns in the detection of veterinary drug residues in poultry and swine in Brazil

Anna Julia Portz; Nilton Silva; Gustavo Lima; Leandro Feijó; Helder Louvandini; Vanessa Peripolli; Renata Vieira; Concepta McManus

doi:10.1590/1809-6891v23e-71763E

Autores

Anna Julia Portz Universidade de Brasília (UnB), Brasília, Distrito Federal, Brasil https://orcid.org/0000-0003-3465-4259
Nilton Silva Universidade de Brasília (UnB), Brasília, Distrito Federal, Brasil https://orcid.org/0000-0002-5817-5175
Gustavo Lima Universidade de Brasília (UnB), Brasília, Distrito Federal, Brasil https://orcid.org/0000-0002-7986-4017
Leandro Feijó Ministério da Agricultura, Pecuária e Abastecimento, Brasília, Distrito Federal , Brasil https://orcid.org/0000-0002-5882-4382
Helder Louvandini Centro de Energia Nuclear na Agricultura, Universidade de São Paulo, Piracicaba, São Paulo, Brasil https://orcid.org/0000-0001-7129-8463
Vanessa Peripolli Instituto Federal Catarinense (IFC), Campus Araquari, Araquari, Santa Catarina, Brasil https://orcid.org/0000-0002-0463-4727
Renata Vieira Universidade de Brasília (UnB), Brasília, Distrito Federal, Brasil https://orcid.org/0000-0003-1720-4467
Concepta McManus Universidade de Brasília (UnB), Brasília, Distrito Federal, Brasil https://orcid.org/0000-0002-1106-8962

DOI:

https://doi.org/10.1590/1809-6891v23e-71763E

Resumo

A Segurança Alimentar é um tema importante para a saúde pública e o comércio internacional de alimentos. Resíduos de medicamentos veterinários e contaminantes ambientais em produtos de origem animal podem causar doenças e toxicidade aguda em organismos expostos a essas substâncias. Este estudo avaliou dados oficiais de monitoramento de resíduos de medicamentos veterinários do Ministério da Agricultura, Pecuária e Abastecimento em tecidos de aves e suínos no período de 2002 a 2014 para verificar padrões ocultos na ocorrência de seis medicamentos comuns (Closantel, Diclazuril, Nicarbazina, Sulfaquinoxalina, Doxiciclina e Sulfametazina). A análise dos dados foi realizada por meio de dois métodos de aprendizado de máquina: árvore de decisão e redes neurais, além da avaliação visual por meio de gráficos e mapas. As taxas de contaminação foram baixas, variando de 0 a 0,66%. Foi identificado um padrão de distribuição espacial das detecções de substâncias por região, mas nenhum padrão de distribuição temporal foi observado. No entanto, as regressões mostraram um aumento nos níveis quando essas substâncias foram detectadas, portanto, o monitoramento deve continuar. No entanto, os resultados mostram que os produtos monitorados durante o período do estudo apresentaram baixo risco à saúde pública.
Palavras-chave: Aprendizagem de máquina; saúde pública; segurança alimentar; resíduos

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